Human Video Motion Transfer (HVMT) aims to, given an image of a source
person, generate his/her video that imitates the motion of the driving person.
Existing methods for HVMT mainly exploit Generative Adversarial Networks (GANs)
to perform the warping operation based on the flow estimated from the source
person image and each driving video frame. However, these methods always
generate obvious artifacts due to the dramatic differences in poses, scales,
and shifts between the source person and the driving person. To overcome these
challenges, this paper presents a novel REgionto-whole human MOtion Transfer
(REMOT) framework based on GANs. To generate realistic motions, the REMOT
adopts a progressive generation paradigm: it first generates each body part in
the driving pose without flow-based warping, then composites all parts into a
complete person of the driving motion. Moreover, to preserve the natural global
appearance, we design a Global Alignment Module to align the scale and position
of the source person with those of the driving person based on their layouts.
Furthermore, we propose a Texture Alignment Module to keep each part of the
person aligned according to the similarity of the texture. Finally, through
extensive quantitative and qualitative experiments, our REMOT achieves
state-of-the-art results on two public benchmarks.

本文提出了一种基于生成对抗网络的全局对齐迁移框架 (REgionto-whole human MOtion Transfer)，通过逐步生成多个身体部位并使用全局对齐模块来对齐源人的比例和位置，再使用纹理对齐模块来保持每个人的部分的相似性，在两个公共基准测试中取得了最先进的结果。

REMOT: 适用于逼真人体动作转移的区域至整体框架

REMOT: A Region-to-Whole Framework for Realistic Human Motion Transfer

Human video motion transfer (HVMT) aims to synthesize videos that one person
imitates other persons' actions. Although existing GAN-based HVMT methods have
achieved great success, they either fail to preserve appearance details due to
the loss of spatial consistency between synthesized and exemplary images, or
generate incoherent video results due to the lack of temporal consistency among
video frames. In this paper, we propose Coarse-to-Fine Flow Warping Network
(C2F-FWN) for spatial-temporal consistent HVMT. Particularly, C2F-FWN utilizes
coarse-to-fine flow warping and Layout-Constrained Deformable Convolution
(LC-DConv) to improve spatial consistency, and employs Flow Temporal
Consistency (FTC) Loss to enhance temporal consistency. In addition, provided
with multi-source appearance inputs, C2F-FWN can support appearance attribute
editing with great flexibility and efficiency. Besides public datasets, we also
collected a large-scale HVMT dataset named SoloDance for evaluation. Extensive
experiments conducted on our SoloDance dataset and the iPER dataset show that
our approach outperforms state-of-art HVMT methods in terms of both spatial and
temporal consistency. Source code and the SoloDance dataset are available at
this https URL

本研究提出了一种名为 Coarse-to-Fine 的流场网络，可以在人类视频运动转移中实现空间 - 时间一致性并支持外貌属性编辑。通过在 SoloDance 公共数据集和 iPER 数据集上进行的广泛实验，证明了该算法在空间和时间一致性方面优于现有技术。